Paperback. Everyone is "doing AI." Most are still copy-pasting tutorial code that breaks the moment a real user touches it. AI Engineer Mastery is the bridge - a pragmatic, opinionated, code-heavy field guide that takes you from your first genai.Client() call to deploying production AI systems with monitoring, evaluation, and safety guardrails.Written by Jegadeesh M, an AI Researcher who's shipped real AI features at scale, this book skips the theory and goes straight to what works: the exact tools, patterns, and workflows used by engineers at companies building with LLMs every day.The complete journey, in 43 chapters: Foundations - Streaming APIs, LangChain, memory, vector databases, RAGAgents - ReAct, MCP, CrewAI, AutoGen, LangGraph, Google ADKProduction - Fine-tuning with LoRA, advanced RAG (hybrid search, reranking, RAGAS), MLOps with MLflow and LangSmith, cloud deployment on Vertex AI / Bedrock / WatsonX. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Bibliographic Details

Title: AI Engineer Mastery (Paperback)
Publisher: Notion Press
Publication Date: 2026
Language: English
ISBN 13: 9798904511722
Binding: Paperback
Condition: new

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Synopsis

Everyone is "doing AI." Most are still copy-pasting tutorial code that breaks the moment a real user touches it. AI Engineer Mastery is the bridge - a pragmatic, opinionated, code-heavy field guide that takes you from your first genai.Client() call to deploying production AI systems with monitoring, evaluation, and safety guardrails.Written by Jegadeesh M, an AI Researcher who's shipped real AI features at scale, this book skips the theory and goes straight to what works: the exact tools, patterns, and workflows used by engineers at companies building with LLMs every day.The complete journey, in 43 chapters: Foundations - Streaming APIs, LangChain, memory, vector databases, RAGAgents - ReAct, MCP, CrewAI, AutoGen, LangGraph, Google ADKProduction - Fine-tuning with LoRA, advanced RAG (hybrid search, reranking, RAGAS), MLOps with MLflow and LangSmith, cloud deployment on Vertex AI / Bedrock / WatsonX.

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